Journal article
Predicting alcohol dependence from multi-site brain structural measures
S Hahn, S Mackey, J Cousijn, JJ Foxe, A Heinz, R Hester, K Hutchinson, F Kiefer, O Korucuoglu, T Lett, CSR Li, E London, V Lorenzetti, L Maartje, R Momenan, C Orr, M Paulus, L Schmaal, R Sinha, Z Sjoerds Show all
Human Brain Mapping | WILEY | Published : 2022
DOI: 10.1002/hbm.25248
Open access
Abstract
To identify neuroimaging biomarkers of alcohol dependence (AD) from structural magnetic resonance imaging, it may be useful to develop classification models that are explicitly generalizable to unseen sites and populations. This problem was explored in a mega-analysis of previously published datasets from 2,034 AD and comparison participants spanning 27 sites curated by the ENIGMA Addiction Working Group. Data were grouped into a training set used for internal validation including 1,652 participants (692 AD, 24 sites), and a test set used for external validation with 382 participants (146 AD, 3 sites). An exploratory data analysis was first conducted, followed by an evolutionary search based..
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Awarded by National Institute of Mental Health
Funding Acknowledgements
Division of Advanced Cyberinfrastructure, Grant/Award Number: OAC-1827314; National Institute of Mental Health, Grant/Award Number: R01 DA018307; National Institute on Alcohol Abuse and Alcoholism, Grant/Award Numbers: R01-AA013892, ZIA AA000125-04 DICB; National Institute on Drug Abuse, Grant/Award Numbers: PL30-1DA024859-01, R01-DA014100, R01-DA020726, R01DA047119, T32DA043593, UL1-RR24925-01; National Institutes of Health, Grant/Award Number: U54 EB020403; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Grant/Award Numbers: VICI grant 453.08.01, VIDI grant 016.08.322, ZonMW grant 31160003, ZonMW grant 31160004, ZonMW grant 31180002, ZonMW grant 91676084; Philip Morris International